Missed TensorFlow Dev Summit? Check out the video playlist. Watch recordings

tfp.mcmc.TransitionKernel

View source on GitHub

Base class for all MCMC TransitionKernels.

This class defines the minimal requirements to efficiently implement a Markov chain Monte Carlo (MCMC) transition kernel. A transition kernel returns a new state given some old state. It also takes (and returns) "side information" which may be used for debugging or optimization purposes (i.e, to "recycle" previously computed results).

Attributes:

  • is_calibrated: Returns True if Markov chain converges to specified distribution.

    TransitionKernels which are "uncalibrated" are often calibrated by composing them with the tfp.mcmc.MetropolisHastings TransitionKernel.

Methods

bootstrap_results

View source

bootstrap_results(
    init_state
)

Returns an object with the same type as returned by one_step(...)[1].

Args:

  • init_state: Tensor or Python list of Tensors representing the initial state(s) of the Markov chain(s).

Returns:

  • kernel_results: A (possibly nested) tuple, namedtuple or list of Tensors representing internal calculations made within this function.

one_step

View source

one_step(
    current_state, previous_kernel_results
)

Takes one step of the TransitionKernel.

Must be overridden by subclasses.

Args:

  • current_state: Tensor or Python list of Tensors representing the current state(s) of the Markov chain(s).
  • previous_kernel_results: A (possibly nested) tuple, namedtuple or list of Tensors representing internal calculations made within the previous call to this function (or as returned by bootstrap_results).

Returns:

  • next_state: Tensor or Python list of Tensors representing the next state(s) of the Markov chain(s).
  • kernel_results: A (possibly nested) tuple, namedtuple or list of Tensors representing internal calculations made within this function.